Complexity of finding embeddings in a k-tree
SIAM Journal on Algebraic and Discrete Methods
Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
A tutorial on learning with Bayesian networks
Proceedings of the NATO Advanced Study Institute on Learning in graphical models
A sufficiently fast algorithm for finding close to optimal clique trees
Artificial Intelligence
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Learning Bayesian networks from data: an information-theory based approach
Artificial Intelligence
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Learning equivalence classes of bayesian-network structures
The Journal of Machine Learning Research
Large-Sample Learning of Bayesian Networks is NP-Hard
The Journal of Machine Learning Research
Decomposition of search for v-structures in DAGs
Journal of Multivariate Analysis
Decomposition of structural learning about directed acyclic graphs
Artificial Intelligence
A graphical model for predicting protein molecular function
ICML '06 Proceedings of the 23rd international conference on Machine learning
On Model Selection Consistency of Lasso
The Journal of Machine Learning Research
A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
The Journal of Machine Learning Research
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm
The Journal of Machine Learning Research
Learning graphical model structure using L1-regularization paths
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Learning bayesian network structure from massive datasets: the «sparse candidate« algorithm
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Causal inference and causal explanation with background knowledge
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Impact of censoring on learning Bayesian networks in survival modelling
Artificial Intelligence in Medicine
An Expert System Based Approach to Modeling and Selecting Requirement Engineering Techniques
WISM '09 Proceedings of the International Conference on Web Information Systems and Mining
A note on minimal d-separation trees for structural learning
Artificial Intelligence
MC4: a tempering algorithm for large-sample network inference
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Review: learning bayesian networks: Approaches and issues
The Knowledge Engineering Review
New skeleton-based approaches for Bayesian structure learning of Bayesian networks
Applied Soft Computing
High-dimensional Gaussian graphical model selection: walk summability and local separation criterion
The Journal of Machine Learning Research
Learning optimal bayesian networks: a shortest path perspective
Journal of Artificial Intelligence Research
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In this paper, we propose a recursive method for structural learning of directed acyclic graphs (DAGs), in which a problem of structural learning for a large DAG is first decomposed into two problems of structural learning for two small vertex subsets, each of which is then decomposed recursively into two problems of smaller subsets until none subset can be decomposed further. In our approach, search for separators of a pair of variables in a large DAG is localized to small subsets, and thus the approach can improve the efficiency of searches and the power of statistical tests for structural learning. We show how the recent advances in the learning of undirected graphical models can be employed to facilitate the decomposition. Simulations are given to demonstrate the performance of the proposed method.